评估、综合与优化客户支持对话

摘要

高效的客户支持不仅需要准确的问题解决能力，还要求遵循专业标准进行结构化且富有同理心的沟通。然而，现有的对话数据集往往缺乏策略性指导，且现实中的服务数据难以获取和标注。为此，我们提出了客户支持对话（CSC）任务，旨在培训客服人员运用明确的支援策略进行回应。我们基于COPC准则构建了一个结构化的CSC框架，定义了五个对话阶段和十二种策略，以引导高质量的互动。在此基础上，我们创建了CSConv，一个包含1,855条真实客户与客服对话的评估数据集，这些对话通过大语言模型（LLMs）重写以体现策略的刻意运用，并进行了相应标注。此外，我们开发了一种角色扮演方法，利用与CSC框架对齐的LLM驱动角色模拟富含策略的对话，生成了训练数据集RoleCS。实验表明，在RoleCS上微调强大的LLMs能显著提升其在CSConv上生成高质量、策略对齐回应的能力。人类评估进一步证实了问题解决能力的提升。所有代码和数据将公开于https://github.com/aliyun/qwen-dianjin。

English

Effective customer support requires not only accurate problem solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service agents to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer-agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution. All code and data will be made publicly available at https://github.com/aliyun/qwen-dianjin.